En-Bing Lin

38 papers receiving 347 citations

Peers

En-Bing Lin
Comparison fields: 5 of 64
  • Modeling and Simulation 126
  • Numerical Analysis 89
  • Applied Mathematics 61
  • Computational Theory and Mathematics 81
  • Computer Vision and Pattern Recognition 64
Replace Jacques Liandrat with:
Jacques Liandrat France
Will Light United Kingdom
A. S. Vasudeva Murthy India
Annika Lang Sweden
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Citations per year

Countries citing papers authored by En-Bing Lin

Since Specialization
Citations

This map shows the geographic impact of En-Bing Lin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by En-Bing Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites En-Bing Lin more than expected).

Fields of papers citing papers by En-Bing Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by En-Bing Lin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by En-Bing Lin. The network helps show where En-Bing Lin may publish in the future.

Co-authors

The 25 scholars most cited alongside En-Bing Lin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with En-Bing Lin Line = papers co-authored together En-Bing Lin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200998
2 201441
3 200139
4 200422
5 201421
6 201718
7 201813
8 201612
9 199711
10 20009
11 20138
12 20128
13
Wavelet Based Methods for Numerical Solutions of Two Dimensional Integral Equations
20147
14 20037
15 20227
16 20215
17 20174
18 20134
19 20183
20 20203

About En-Bing Lin

En-Bing Lin is a scholar working on Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Artificial Intelligence, Applied Mathematics and Signal Processing, having authored 54 papers that have together received 372 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (16 papers), Rough Sets and Fuzzy Logic (13 papers), Fractional Differential Equations Solutions (6 papers), Blind Source Separation Techniques (4 papers), Advanced Numerical Analysis Techniques (4 papers), Mathematical Analysis and Transform Methods (4 papers), Analytic and geometric function theory (3 papers) and Neural Networks and Applications (3 papers). The work is most often cited by research in Modeling and Simulation (126 citations), Numerical Analysis (89 citations), Applied Mathematics (61 citations), Computational Theory and Mathematics (81 citations) and Computer Vision and Pattern Recognition (64 citations). En-Bing Lin has collaborated with scholars based in United States, Taiwan and United Arab Emirates. Frequent co-authors include Yu-Ru Syau, Xu Zhou, Mutaz Mohammad, Paul C. Liu, Xiaolin Zhou, Churn‐Jung Liau, Andrzej Skowron, A. Xiuhua, Jong‐Won Kim and Edward E. McKee. Their work appears in journals such as Numerical Methods for Partial Differential Equations, Journal of Computational and Applied Mathematics, PLoS ONE, Journal of Inequalities and Applications and International Journal of Uncertainty Fuzziness and Knowledge-Based Systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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